The human brain in general, and the cerebral cortex in particular, remains the most sophisticated computational system known to man. Elucidating the mechanisms underlying the cerebral cortex's ability to process information is critical for understanding both normal cortical processing, and a myriad of neurological disorders produced by abnormal cortical function. The current research begins with the assumption that a necessary step towards understanding cortical processing will be to elucidate two related problems: temporal processing and neural dynamics. First, temporal processing (the decoding of temporal information) is of fundamental importance for most forms of sensory processing, perhaps most notably speech. Yet the neural mechanisms underlying even simple forms of temporal computations are not known. Second, neural dynamics (evolving changes in the spatial-temporal patterns of neural activity) is thought to play a pivotal role in many forms of neural computation, including temporal processing. However, given the inherently complex and highly nonlinear nature of neural activity in recurrent networks composed of spiking neurons, we do not yet understand (1) how dynamics emerges and is controlled (e.g. to avoid epileptic activity), and (2) how dynamics is tuned in an experience- dependent manner to allow learning. It is generally assumed that synaptic plasticity (as well as plasticity at other loci) ultimately underlies the control and behavior of cortical networks. However, while a number of forms of plasticity have provided powerful learning rules when implemented into feed-forward networks, the implementation of effective learning rules in recurrent networks has proven largely intractable. In the current proposal we will examine whether multiple learning rules in parallel, operating synergistically, can lead to effective learning in recurrent networks. We will pay particular attention to the simultaneous control of excitatory and inhibitory synapses, which is hypothesized to be critical in recurrent networks. The proposal consists of two Aims. In the first Aim we will use a set of synaptic learning rules to train a large-scale recurrent network to develop stable and robust responses to spoken phonemes - in other words to encode complex stimuli in a spatial-temporal pattern of spikes. The second Aim will use a novel learning rule to teach output units to respond selectively to classes of phonemes - that is, to decode the patterns of the cortical network. If progress is made in this direction the research described here will enhance not only our understanding of normal cortical processing, but in the understanding of abnormal cortical states that arise in neurological disorders characterized by abnormal temporal processing and neural dynamics; such diseases include autism, Fragile X, dyslexia, and epilepsy. Behavior and cognition are ultimately an emergent property of the dynamic interaction of hundreds of thousands of neurons embedded in complex networks. While significant progress has been made towards understanding cellular and synaptic properties in isolation, elucidating how the activity of hundreds of thousands of neurons underlie cortical computations remains an elusive and fundamental goal in neuroscience. The research described here will use large-scale simulations of cortical networks to define the synaptic learning rules that allow computations to emerge from recurrent cortical networks. Specifically, the ability of artificial neural networks to learn to discriminate spoken phonemes will be examined. Understanding how computations emerge from massive networks of interconnected elements is a necessary step towards understanding normal cortical function, as well as the pathological cortical abnormalities observed in a myriad of neurological disorders. [unreadable] [unreadable] [unreadable]